What is the impact of learning rate schedules in Q-Learning convergence?

Learning rate schedules control convergence speed and stability by adjusting update magnitude over time.

A constant learning rate can lead to oscillations or divergence, while a decaying learning rate ensures convergence by reducing update size as the Q-function stabilizes. Schedules like exponential decay or inverse time decay are commonly used in both tabular and deep Q-learning setups.